Therefore, plot object aesthetics can be colors, sizes, transparencies, placement, etc. In ggplot those details are called “themes” and are adjusted within a theme() command (see this section). Note that “aesthetic” here refers to the data being plotted in geoms/shapes - not the surrounding display such as titles, axis labels, background color, that you might associate with the word “aesthetics” in common English. It refers to a visual property of plotted data. In ggplot terminology a plot “aesthetic” has a specific meaning. This geom inherits the mappings from the ggplot() command above - it knows the axis-column assignments and proceeds to visualize those relationships as points on the canvas. A shape is created with the “geom” function geom_point(). In the mapping = aes() argument the column age is mapped to the x-axis, and the column wt_kg is mapped to the y-axis.Īfter a +, the plotting commands continue. The mappings you provide to mapping must be wrapped in the aes() function, so you would write something like mapping = aes(x = col1, y = col2), as shown below.īelow, in the ggplot() command the data are set as the case linelist. This “mapping” occurs with the mapping = argument. For most geoms, the essential components that must be mapped to columns in the data are the x-axis, and (if necessary) the y-axis. Most geom functions must be told what to use to create their shapes - so you must tell them how they should map (assign) columns in your data to components of the plot like the axes, shape colors, shape sizes, etc. We will explain each component in the sections below.
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